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NEW APPROACH FOR EMPHYSEMA PATTERN DETECTION IN COMPUTED TOMOGRAPHY IMAGES
TABLE OF CONTENTS
Title Page i
Declaration ii
Approval Page iii
Dedication iv
Abstract vi
Table of Contents vii
CHAPTER ONE: INTRODUCTION
1.1 Introduction 1
1.2 Background of the study 3
1.3 Statement of the General Problem 4
1.4 Objective of the study 5
1.5 Significance of the study 5
1.6 Statement of hypothesis 6
1.7 Scope of the study 6
1.8 Limitation of the study 7
1.9 Definition of terms 7
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction 9
2.1 Review of related literature 9
2.2 Theoretical framework
2.3 Summary of review 33
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction 35
3.2 Research design 35
3.3 Area of study 35
3.4 Population of the study 36
3.5 Sample size 36
3.6 Instrument for data collection 36
3.7 Reliability of the instrument 37
3.8 Validity of the Instrument 38
3.9 Method of data Collection 38
3.10 Method of Data Analysis 39
CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS
4.1 Introduction 41
4.2 Characteristics of the respondents 41
4.3 Presentation of Data Analysis 43
4.4 Discussion of Findings 48
4.5 Summary of findings 49
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary 51
5.2 Conclusion 52
5.3 Recommendation 53
Biography 54
Appendix 56
Abstract:
Emphysema is a chronic lung disease characterized by the destruction of lung tissue, leading to airflow limitation and impaired respiratory function. Early detection of emphysema patterns in computed tomography (CT) images plays a crucial role in the diagnosis and treatment of this condition. This abstract presents a new approach for emphysema pattern detection in CT images, aiming to improve the accuracy and efficiency of diagnosis.
The proposed approach utilizes advanced image processing and machine learning techniques to automatically identify and quantify emphysema patterns in CT images. Initially, a preprocessing step is applied to enhance the quality of CT images by reducing noise and artifacts. Next, lung segmentation is performed to isolate the lung regions of interest.
Subsequently, a combination of texture analysis and feature extraction methods is applied to characterize the emphysema patterns. Texture features such as mean, variance, entropy, and co-occurrence matrices are extracted from the segmented lung regions. These features capture the spatial distribution and structural properties of emphysema, enabling effective discrimination between healthy and emphysematous lung tissue.
To achieve accurate classification, a machine learning algorithm, such as support vector machines (SVM) or convolutional neural networks (CNN), is trained on a labeled dataset of CT images. The classifier learns the discriminative patterns associated with emphysema, enabling it to classify new CT images as either healthy or emphysematous.
The performance of the proposed approach is evaluated using a comprehensive dataset of CT images from patients with confirmed emphysema. The results demonstrate the effectiveness of the proposed method in accurately detecting and quantifying emphysema patterns. The approach achieves high sensitivity and specificity, providing valuable information for clinicians in the diagnosis and monitoring of emphysema.
In conclusion, the proposed approach presents a novel and efficient method for emphysema pattern detection in CT images. By combining advanced image processing techniques and machine learning algorithms, it enables accurate identification and quantification of emphysema patterns, facilitating early diagnosis and personalized treatment planning for patients with emphysema.
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